A Fuzzy Control Chart Approach for Attributes and Variables

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach ha

Automated software quality visualisation using fuzzy logic techniques

In the past decade there has been a concerted effort by the software industry to improve the quality of its products. This has led to the inception of various techniques with which to control and measure the process involved in software development. Methods like the Capability Maturity Model have introduced processes and strategies that require measurement in the form of software metrics. With the ever increasing number of software metrics being introduced by capability based processes, software development organisations are finding it more difficult to understand and interpret metric scores. This is particularly problematic for senior management and project managers where analysis of the actual data is not feasible. This paper proposes a method with which to visually represent metric scores so that managers can easily see how their organisation is performing relative to quality goals set for each type of metric. Acting primarily as a proof of concept and prototype, we suggest ways in which real customer needs can be translated into a feasible technical solution. The solution itself visualises metric scores in the form of a tree structure and utilises Fuzzy Logic techniques, XGMML, Web Services and the .NET Framework. Future work is proposed to extend the system from the prototype stage and to overcome a problem with the masking of poor scores

An Intelligent Approach to Prioritize Logistics Requirements in Food Industry

The aim of this study is to analyze dairy food industry and to determine the priorities of important logistics requirements (LR) based on customer requirements as a part of a supply management system. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of the priorities of the LR is an important issue during QFD process for product or service design. For this reason, in this work, an integrated approach integrating fuzzy logic and QFD methods is proposed to identify and prioritize the LR in dairy food industry for the improvement of customer satisfaction. In addition, a case study in Turkish dairy food industry is given to illustrate the proposed approach for potential readers

FUZZY CONTROL CHARTS FOR VARIABLE AND ATTRIBUTE QUALITY CHARACTERISTICS

ABSTRACT. This paper addresses the design of control charts for both variable ( x chart) and attribute (u and c charts) quality characteristics, when there is uncertainty about the process parameters or sample data. Derived control charts are more flexible than the strict crisp case, due to the ability of encompassing the effects of vagueness in form of the degree of expert's presumption. We extend the use of proposed fuzzy control charts in case of linguistic data using a developed defuzzifier index, which is based on the metric distance between fuzzy sets

Quality control charts under random fuzzy measurements

Includes bibliographical references. .We consider statistical process control charts as tools that statistical process control utilizes for monitoring changes; identifying process variations and their causes in industrial processes (manufacturing processes) and which help manufacturers to take the appropriate action, rectify problems or improve manufacturing processes so as to produce good quality products. As an essential tool, researchers have always paid attention to the development of process control charts. Also, the sample sizes required for establishing control charts are often under discussion depending on the field of study. Of late, the problem of Fuzziness and Randomness often brought into modern manufacturing processes by the shortening product life cycles and diversification (in product designs, raw material supply etc) has compelled researchers to invoke quality control methodologies in their search for high customer satisfaction and better market shares (Guo et al 2006). We herein focus our attention on small sample sizes and focus on the development of quality control charts in terms of the Economic Design of Quality Control Charts; based on credibility measure theory under Random Fuzzy Measurements and Small Sample Asymptotic Distribution Theory. Economic process data will be collected from the study of Duncan (1956) in terms of these new developments as an illustrative example. or/Producer, otherwise they are undertaken with respect to the market as a whole. The techniques used for tackling the complex issues are diverse and wide-ranging as ascertained from the existing literature on the subject. The global ideology focuses on combining two streams of thought: the production optimisation and equilibrium techniques of the old monopolistic, cost-saving industry and; the new dynamic profit-maximising and risk-mitigating competitive industry. Financial engineering in a new and poorly understood market for electrical power must now take place in conjunction with - yet also constrained by - the physical production and distribution of the commodity

Testing satistical hipotheses in fuzzy environment

In traditional statistics all parameters of the mathematical model and possible observations should be well defined. Sometimes such assumption appears too rigid for the real-life problems, especially while dealing with linguistic data or imprecise requirements. To relax this rigidity fuzzy methods are incorporated into statistics. We review hitherto existing achievements in testing statistical hypotheses in fuzzy environment, point out their advantages or disadvantages and practical problems. We propose also a formalization of that decision problem and indicate the directions of further investigations in order to construct a more general theory

New Challenges in Neutrosophic Theory and Applications

Neutrosophic theory has representatives on all continents and, therefore, it can be said to be a universal theory. On the other hand, according to the three volumes of “The Encyclopedia of Neutrosophic Researchers” (2016, 2018, 2019), plus numerous others not yet included in Encyclopedia book series, about 1200 researchers from 73 countries have applied both the neutrosophic theory and method. Neutrosophic theory was founded by Professor Florentin Smarandache in 1998; it constitutes further generalization of fuzzy and intuitionistic fuzzy theories. The key distinction between the neutrosophic set/logic and other types of sets/logics lies in the introduction of the degree of indeterminacy/neutrality (I) as an independent component in the neutrosophic set. Thus, neutrosophic theory involves the degree of membership-truth (T), the degree of indeterminacy (I), and the degree of non-membership-falsehood (F). In recent years, the field of neutrosophic set, logic, measure, probability and statistics, precalculus and calculus, etc., and their applications in multiple fields have been extended and applied in various fields, such as communication, management, and information technology. We believe that this book serves as useful guidance for learning about the current progress in neutrosophic theories. In total, 22 studies have been presented and reflect the call of the thematic vision. The contents of each study included in the volume are briefly described as follows. The first contribution, authored by Wadei Al-Omeri and Saeid Jafari, addresses the concept of generalized neutrosophic pre-closed sets and generalized neutrosophic pre-open sets in neutrosophic topological spaces. In the article “Design of Fuzzy Sampling Plan Using the Birnbaum-Saunders Distribution”, the authors Muhammad Zahir Khan, Muhammad Farid Khan, Muhammad Aslam, and Abdur Razzaque Mughal discuss the use of probability distribution function of Birnbaum–Saunders distribution as a proportion of defective items and the acceptance probability in a fuzzy environment. Further, the authors Derya Bakbak, Vakkas Uluc¸ay, and Memet S¸ahin present the “Neutrosophic Soft Expert Multiset and Their Application to Multiple Criteria Decision Making” together with several operations defined for them and their important algebraic properties. In “Neutrosophic Multigroups and Applications”, Vakkas Uluc¸ay and Memet S¸ahin propose an algebraic structure on neutrosophic multisets called neutrosophic multigroups, deriving their basic properties and giving some applications to group theory. Changxing Fan, Jun Ye, Sheng Feng, En Fan, and Keli Hu introduce the “Multi-Criteria Decision-Making Method Using Heronian Mean Operators under a Bipolar Neutrosophic Environment” and test the effectiveness of their new methods. Another decision-making study upon an everyday life issue which empowered us to organize the key objective of the industry developing is given in “Neutrosophic Cubic Einstein Hybrid Geometric Aggregation Operators with Application in Prioritization Using Multiple Attribute Decision-Making Method” written by Khaleed Alhazaymeh, Muhammad Gulistan, Majid Khan, and Seifedine Kadry

Fuzzy Process Control And Development Of Some Models For Fuzzy Control Charts

Tez (Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2006Thesis (PhD) -- İstanbul Technical University, Institute of Science and Technology, 2006Bu çalışmada, bulanık kümeler teorisi kullanılarak belirsizlik içeren dilsel verilerin kontrol diyagramlarına yeni yaklaşımlar geliştirilmiştir. Belirsizlik içeren dilsel veriler, bulanık sayılarla ifade edilmiştir. Dilsel veriler için bulanık kontrol diyagramları α-kesim yaklaşımı kullanılarak geliştirilmiş ve bu suretle muayene sıklığı tanımlanmıştır. Bulanık kontrol diyagramlarının oluşturulmasında, bulanık verilerin taşıdığı bilgilerin kaybolmasını önlemek amacıyla “Direkt Bulanık Yaklaşım” geliştirilmiştir. Bulanık verilerin kontrol diyagramındaki normal olmayan davranış testleri için bulanık bir yaklaşım geliştirilmiştir. Önerilen yaklaşımların pratik kullanımlarının yansıtılması açısından gerçek verilere dayalı nümerik örnekler sunulmuştur.In this study, process control charts under linguistic, vague, and uncertain data are developed in the light of the Fuzzy Set Theory. Linguistic or uncertain data are represented by the use of fuzzy numbers. Fuzzy control charts for the linguistic data are proposed and integrated with the α-cut approach of fuzzy sets in order to set the degree of tightness of the inspection. A new approach called direct fuzzy approach to fuzzy control charts is modeled in order to prevent the loss of information of the fuzzy data during the construction of control charts. Finally, fuzzy unnatural pattern analyses are developed to monitor the abnormal patterns of the fuzzy data on the control charts. Numerical examples using the data of a real case are also given to highlight the practical usage of the proposed approaches.DoktoraPh